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In recent years several models for financial high-frequency data have been proposed. One of the most known models for this type of applications is the ACM-ACD model. This model focuses on modelling the underlying joint distribution of both duration and price changes between consecutive transactions. However this model imposes distributional assumptions and its number of parameters increases rapidly (producing a complex and slow adjustment process). Therefore, we propose using two machine learning models, that will work sequentially, based on the ACM-ACD model. The results show a comparable performance, achieving a better performance in some cases. Also the proposal achieves a significatively more rapid convergence. The proposal is validated with a well-known financial data set.

In this article a maturity model is proposed, named OD-MM (Open Data Maturity Model) to assess the commitment and capabilities of public agencies in pursuing the principles and practices of open data. The OD-MM model has a three level hierarchical structure, called domains, sub-domains and critical variables. Four capacity levels are defined for each of the 33 critical variables distributed in nine sub-domains in order to determine the organization maturity level. The model is a very valuable diagnosis tool for public services, given it shows all weaknesses and the way (a roadmap) to progress in the implementation of open data

The tasks execution scheduling is a common problem in computer science. The typical problem, as in industrial or computer processing applications, has some restrictions that are inapplicable for certain cases. For example, all available tasks have to be executed at some point, and ambient factors do not affect the execution order. In the astronomical observations field, projects are scheduled as observation blocks, and their execution depends on parameters like science goals priority and target visibility, but is also restricted by external factors: atmospheric conditions, equipment failure, etc. A telescope scheduler is mainly in charge of handling projects, commanding the telescope’s high level movement to targets, and starting data acquisition. With the growth of observatories’ capacities and maintenance costs, it is now mandatory to optimize the observation time allocation. Currently, at professional observatories there is still strong human intervention dependency, with no fully automatic solution so far. This paper aims to describe the dynamic scheduling problem in astronomical observations, and to provide a survey on existing solutions, opening some new application opportunities for computer science.

This study introduces a new method to calculate the most efficient point of operation of a Permanent Magnet Synchronous Machine (PMSM). The proposed method combines the characteristics of an analytical solution known as the Maximum Torque Per Ampere (MTPA) curve with compensation through an Artificial Neural Network (ANN). By employing this hybrid approach, it is possible to identify and optimize the operating point, even when there is uncertainty in the parameters used in the analytical model.

E-government research deals with ‘wicked’ problems that require multidisciplinary approaches to gain a full understanding. One of the main challenges of e-government is to induce change in the structure of public organizations to realize its full potential. This paper investigates e-government induced change using two complementary theoretical lenses applied to an egovernment case study. We use organization theories to explore aspects of organizational structure that may change when implementating e-government and structuration theory to investigate how these aspects are affected by human action within its social structure. This combination allows us to investigate the discrepancy between the ambitions of e-government induced change and the actual changes accomplished in practice. Our analysis shows that using these two frames gives us better insight into the thorny subject of e-government than using a single theory. Further research should look into how these theories can be used to deepen our knowledge of e-government.

In this paper we explore the use of the cluster analysis in segmentation problems, that is, identifying image points with an indication of the region or class they belong to. The proposed algorithm uses the well known agglomerative hierarchical cluster analysis algorithm in order to form clusters of pixels, but modified so as to cope with the high dimensionality of the problem. The results of different stages of the algorithm are saved, thus retaining a collection of segmented images ordered by degree of segmentation. This allows the user to view the whole collection and choose the one that suits him best for his particular application.

In this paper we present a method to detect edges in images. The method consists of using a 3x3 pixel mask to scan the image, moving it from left to right and from top to bottom, one pixel at a time. Each time it is placed on the image, an agglomerative hierarchical cluster analysis is applied to the eight outer pixels. When there is more than one cluster, it means that window is on an edge, and the central pixel is marked as an edge point. After scanning all the image, we obtain a new image showing the marked pixels around the existing edges of the image. Then a thinning algorithm is applied so that the edges are well defined. The method results to be particularly efficient when the image is contaminated. In those cases, a previous restoration method is applied.

Internet advertising continues to show signs of healthy growth despite the current economic downturn, but online advertisements are often considered undesirable by most users. In this study, we focus on the impact these online advertisements have on users in Chile. The study was conducted in a laboratory setting with 80 student subjects. Results are helpful for both researchers and practitioners.

Most component-based approaches to elaborate software require complete and consistent descriptions of components, but in practical settings components information is incomplete, imprecise and changing, and requirements may be likewise. More realistically deployable are approaches that combine exploration of candidate architectures with their evaluation vis-a-vis requirements, and deal with the fuzzy ness of available component information. This article presents an approach to systematic generation, evaluation and re-generation of component assemblies, using potentially incomplete, imprecise, unreliable and changing descriptions of requirements and components. The key ideas are representation of NFRs using architectural policies, systematic reification of policies into mechanisms and components that implement them, multi-dimensional characterizations of these three levels, and catalogs of them. The Azimut framework embodies these ideas and enables traceability of architecture by supporting architecture-level reasoning, and allows architects to engage into systematic exploration of design spaces. A detailed illustrative example illustrates the approach.

This work presents a new method for feature extraction of iris images to improve the identification process. The valuable information of the iris is intrinsically located in its natural texture, and preserving and extracting the most relevant features is of paramount importance. The technique consists in several steps from adquisition up to the person identification. Our contribution consists in a multimodal algorithm where a fragmentation of the normalized iris image is performed and, afterwards, regional statistical descriptors with Self-Organizing-Maps are extracted. By means of a biometric fusion of the resulting descriptors, the features of the iris are compared and classified. The results with the iris data set obtained from the Bath University repository show an excellent accuracy reaching up to 99.867%.

The design of a neural network is presented for the recognition of six kinds of minerals (chalcopyrite, chalcosine, covelline, bornite, pyrite, and energite) and to determine the percentage of these minerals from a digitized image of a rock sample. The input to the neural network corresponds to the histogram of the region of interest selected by the user from the image that it is desired to recognize, which is processed by the neural network, identifying one of the six minerals learned. The network’s training process took place with 160 regions of interest selected from digitized photographs of mineral samples. The recognition of the different types of minerals in the samples was tested with 240 photographs that were not used in the network’s training. The results showed that 97% of the images used to train the network were recognized correctly in the percentage mode. Of the new images, the network was capable of recognizing correctly 91% of the samples.

Structured Peer to Peer overlays have shown to be a very good solution for building very large scale distributed information systems. Most of them are based on Distributed Hash Tables (DHTs) that provide an easy way to manage replicas, thus facilitating high availability of data as well as fault tolerance. However, DHTs can also be affected by some well known Distributed Denial of Services attacks that can lead to almost complete unavailability of the stored objects. Very few powerful solutions exist for this kind of security weakness, and increasing the number of replicas for a given object seems to be the best known one. In this paper, we show how a recursive replicating schema can provide a good solution for this kind of attack.

The precise knowledge of the statistical properties of synthetic aperture radar (SAR) data plays a central role in image processing and understanding. These properties can be used for discriminating types of land uses and to develop specialized filters for speckle noise reduction, among other applications. In this work we assume the distribution G0 A as the universal model for multilook amplitude SAR images under the multiplicative model. We study some important properties of this distribution and some classical estimators for its parameters, such as Maximum Likelihood (ML) estimators, but they can be highly influenced by small percentages of ‘outliers’, i.e., observations that do not fully obey the basic assumptions. Hence, it is important to find Robust Estimators. One of the best known classes of robust techniques is that of M estimators, which are an extension of the ML estimation method. We compare those estimation procedures by means of a Monte Carlo experiment.

The Self Organizing Map (SOM) model is an unsupervised learning neural network that has been successfully applied as a data mining tool. The advantages of the SOMs are that they preserve the topology of the data space, they project high dimensional data to a lower dimension representation scheme, and are able to find similarities in the data. However, the learning algorithm of the SOM is sensitive to the presence of noise and outliers as we will show in this paper. Due to the influence of the outliers in the learning process, some neurons (prototypes) of the ordered map get located far from the majority of data, and therefore, the network will not effectively represent the topological structure of the data under study. In this paper, we propose a variant to the learning algorithm that is robust under the presence of outliers in the data by being resistant to these deviations. We call this algorithm Robust SOM (RSOM). We will illustrate our technique on synthetic and real data sets.

The Neural Gas (NG) is a Vector Quantization technique where a set of prototypes self organize to represent the topology structure of the data. The learning algorithm of the Neural Gas consists in the estimation of the prototypes location in the feature space based in the stochastic gradient descent of an Energy function. In this paper we show that when deviations from idealized distribution function assumptions occur, the behavior of the Neural Gas model can be drastically affected and will not preserve the topology of the feature space as desired. In particular, we show that the learning algorithm of the NG is sensitive to the presence of outliers due to their influence over the adaptation step. We incorporate a robust strategy to the learning algorithm based on M-estimators where the influence of outlying observations are bounded. Finally we make a comparative study of several estimators where we show the superior performance of our proposed method over the original NG, in static data clustering tasks on both synthetic and real data sets.

The Neural Gas (NG) is a Vector Quantization technique where a set of prototypes self organize to represent the topology structure of the data. The learning algorithm of the Neural Gas consists in the estimation of the prototypes location in the feature space based in the stochastic gradient descent of an Energy function. In this paper we show that when deviations from idealized distribution function assumptions occur, the behavior of the Neural Gas model can be drastically affected and will not preserve the topology of the feature space as desired. In particular, we show that the learning algorithm of the NG is sensitive to the presence of outliers due to their influence over the adaptation step. We incorporate a robust strategy to the learning algorithm based on M-estimators where the influence of outlying observations are bounded. Finally we make a comparative study of several estimators where we show the superior performance of our proposed method over the original NG, in static data clustering tasks on both synthetic and real data sets.

The architectural design of neuro-fuzzy models is one of the major concern in many important applications. In this work we propose an extension to Rogers’s ANFIS model by providing it with a selforganizing mechanism. The main purpose of this mechanism is to adapt the architecture during the training process by identifying the optimal number of premises and consequents needed to satisfy a user’s performance criterion. Using both synthetic and real data, our proposal yields remarkable results compared to the classical ANFIS.

Semi-Supervised Learning is one of the most popular and emerging issues in Machine Learning. Since it is very costly to label large amounts of data, it is useful to use data sets without labels. For doing that, normally we uses Semi-Supervised Learning to improve the performance or efficiency of the classification algorithms. This paper intends to use the techniques of Semi-Supervised Learning to boost the performance of the Robust Alternating AdaBoost algorithm. We introduce the algorithm RADA+ and compare it with RADA, re- porting the performance results using synthetic and real data sets, the latter obtained from a benchmark site.

This research aims to determine why people use Social Networks using an adaptation of the UTAUT2 model. The proposed model considers Subjective Norm, Perceived Playfulness, Perceived Ease of Use, and Perceived Usefulness as predictors of the Intention to Use. Five social networks were chosen in order to carry out this research: Facebook, Twitter, Instagram, WhatsApp, and LinkedIn. Findings shows that social networks are more useful to serve his or her purposes when more people close to the individual are using them. Perceived Playfulness proves to be a strong predictor of Intention to Use Facebook, Instagram, and WhatsApp, all these social networks are used for leisure purposes. Perceived Usefulness proves to be the most powerful predictor for Intention to Use in LinkedIn, this social network is mainly used for work purposes. Finally, both Perceived Playfulness and Perceived Usefulness are good predictors of Intention to Use Twitter. Implications are discussed.